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Make sure intervals start low enough to include the minimum value and extend high enough to include the maximum value. The intervals should not overlap or have any gaps. Between 5 and 10 intervals is reasonable.Greater than 10 or fewer than 5 intervals can result in a display that is not very helpful for seeing patterns in the data.

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Q: What are some things to remember when determining the interval for a histogram?
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What are the advantages of a small confidence interval in statistics?

The smaller the confidence interval, the more certain you are of the answers. Remember confidence level and confidence interval (margin of error) are 2 separate things. So if you are using an industry standard confidence level of 95% and 5% margin of error in a standard statistical table, then you could say, for example, with 95% certainty that 60% of those polled would vote for John McCain. Another way of saying this is even though you did not poll everyone (if you did, it would then become a very expensive census), you can say with a high degree of certainty (95% certainty) that 55% to 65% of those polled will vote for Johnny (sadly).


Why does a histogram has no space between its bars?

Because histograms show grouped data which is used for grouping certain things that people need. Also it is because it is easier to figure out the grouped data otherwise you would have to draw in at least 5 BAR GRAPHS.


What is the difference between interval data analysis and ratio data analysis?

Data comes in various sizes and shapes. Two of them are Interval and Ratio. Interval is a measurement where the difference between two values is meaningful and follows a linear scale. For example: in physics, temperature 0.0 on either F or C does not mean 'no temperature'; in biology, a pH of 0.0 does not mean 'no acidity'. Interval data is continuous data where differences are interpretable, ordered, and constant scale, but there is no 'natural' zero. Ratio is the relation in degree or number between two similar things or a relationship between two quantities, ordered, constant scale, with natural zero. Ratio data is interpretable. Ratio data has a natural zero. A good example is birth weight in kg. The distinctions between interval and ratio data are slight. Certain specialized statistics, such as a geometric mean and a coefficient of variation can only be applied to ratio data.


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What is the relationship between confidence interval and standard deviation?

Short answer, complex. I presume you're in a basic stats class so your dealing with something like a normal distribution however (or something else very standard). You can think of it this way... A confidence interval re-scales margin of likely error into a range. This allows you to say something along the lines, "I can say with 95% confidence that the mean/variance/whatever lies within whatever and whatever" because you're taking into account the likely error in your prediction (as long as the distribution is what you think it is and all stats are what you think they are). This is because, if you know all of the things I listed with absolute certainty, you are able to accurately predict how erroneous your prediction will be. It's because central limit theory allow you to assume statistically relevance of the sample, even given an infinite population of data. The main idea of a confidence interval is to create and interval which is likely to include a population parameter within that interval. Sample data is the source of the confidence interval. You will use your best point estimate which may be the sample mean or the sample proportion, depending on what the problems asks for. Then, you add or subtract the margin of error to get the actual interval. To compute the margin of error, you will always use or calculate a standard deviation. An example is the confidence interval for the mean. The best point estimate for the population mean is the sample mean according to the central limit theorem. So you add and subtract the margin of error from that. Now the margin of error in the case of confidence intervals for the mean is za/2 x Sigma/ Square root of n where a is 1- confidence level. For example, confidence level is 95%, a=1-.95=.05 and a/2 is .025. So we use the z score the corresponds to .025 in each tail of the standard normal distribution. This will be. z=1.96. So if Sigma is the population standard deviation, than Sigma/square root of n is called the standard error of the mean. It is the standard deviation of the sampling distribution of all the means for every possible sample of size n take from your population ( Central limit theorem again). So our confidence interval is the sample mean + or - 1.96 ( Population Standard deviation/ square root of sample size. If we don't know the population standard deviation, we use the sample one but then we must use a t distribution instead of a z one. So we replace the z score with an appropriate t score. In the case of confidence interval for a proportion, we compute and use the standard deviation of the distribution of all the proportions. Once again, the central limit theorem tells us to do this. I will post a link for that theorem. It is the key to really understanding what is going on here!

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